INN Hotels Project¶

Context¶

A significant number of hotel bookings are called-off due to cancellations or no-shows. The typical reasons for cancellations include change of plans, scheduling conflicts, etc. This is often made easier by the option to do so free of charge or preferably at a low cost which is beneficial to hotel guests but it is a less desirable and possibly revenue-diminishing factor for hotels to deal with. Such losses are particularly high on last-minute cancellations.

The new technologies involving online booking channels have dramatically changed customers’ booking possibilities and behavior. This adds a further dimension to the challenge of how hotels handle cancellations, which are no longer limited to traditional booking and guest characteristics.

The cancellation of bookings impact a hotel on various fronts:

  • Loss of resources (revenue) when the hotel cannot resell the room.
  • Additional costs of distribution channels by increasing commissions or paying for publicity to help sell these rooms.
  • Lowering prices last minute, so the hotel can resell a room, resulting in reducing the profit margin.
  • Human resources to make arrangements for the guests.

Objective¶

The increasing number of cancellations calls for a Machine Learning based solution that can help in predicting which booking is likely to be canceled. INN Hotels Group has a chain of hotels in Portugal, they are facing problems with the high number of booking cancellations and have reached out to your firm for data-driven solutions. You as a data scientist have to analyze the data provided to find which factors have a high influence on booking cancellations, build a predictive model that can predict which booking is going to be canceled in advance, and help in formulating profitable policies for cancellations and refunds.

Data Description¶

The data contains the different attributes of customers' booking details. The detailed data dictionary is given below.

Data Dictionary

  • Booking_ID: unique identifier of each booking
  • no_of_adults: Number of adults
  • no_of_children: Number of Children
  • no_of_weekend_nights: Number of weekend nights (Saturday or Sunday) the guest stayed or booked to stay at the hotel
  • no_of_week_nights: Number of week nights (Monday to Friday) the guest stayed or booked to stay at the hotel
  • type_of_meal_plan: Type of meal plan booked by the customer:
    • Not Selected – No meal plan selected
    • Meal Plan 1 – Breakfast
    • Meal Plan 2 – Half board (breakfast and one other meal)
    • Meal Plan 3 – Full board (breakfast, lunch, and dinner)
  • required_car_parking_space: Does the customer require a car parking space? (0 - No, 1- Yes)
  • room_type_reserved: Type of room reserved by the customer. The values are ciphered (encoded) by INN Hotels.
  • lead_time: Number of days between the date of booking and the arrival date
  • arrival_year: Year of arrival date
  • arrival_month: Month of arrival date
  • arrival_date: Date of the month
  • market_segment_type: Market segment designation.
  • repeated_guest: Is the customer a repeated guest? (0 - No, 1- Yes)
  • no_of_previous_cancellations: Number of previous bookings that were canceled by the customer prior to the current booking
  • no_of_previous_bookings_not_canceled: Number of previous bookings not canceled by the customer prior to the current booking
  • avg_price_per_room: Average price per day of the reservation; prices of the rooms are dynamic. (in euros)
  • no_of_special_requests: Total number of special requests made by the customer (e.g. high floor, view from the room, etc)
  • booking_status: Flag indicating if the booking was canceled or not.

Importing necessary libraries and data¶

In [232]:
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

# To build linear model for statistical analysis and prediction
import statsmodels.stats.api as sms
from statsmodels.stats.outliers_influence import variance_inflation_factor
import statsmodels.api as sm
from statsmodels.tools.tools import add_constant

# Library to split data
from sklearn.model_selection import train_test_split

# To get diferent metric scores
from sklearn import metrics
from sklearn.metrics import accuracy_score, roc_curve, confusion_matrix, roc_auc_score

# Decision Tree packages
from sklearn.tree import DecisionTreeClassifier
from sklearn import tree

%load_ext nb_black

The nb_black extension is already loaded. To reload it, use:
  %reload_ext nb_black
In [2]:
# Importing the Dataset
df = pd.read_csv("INNHotelsGroup.csv")

df.head(10)  # Display first 10 rows of the dataset
Out[2]:
Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests booking_status
0 INN00001 2 0 1 2 Meal Plan 1 0 Room_Type 1 224 2017 10 2 Offline 0 0 0 65.00 0 Not_Canceled
1 INN00002 2 0 2 3 Not Selected 0 Room_Type 1 5 2018 11 6 Online 0 0 0 106.68 1 Not_Canceled
2 INN00003 1 0 2 1 Meal Plan 1 0 Room_Type 1 1 2018 2 28 Online 0 0 0 60.00 0 Canceled
3 INN00004 2 0 0 2 Meal Plan 1 0 Room_Type 1 211 2018 5 20 Online 0 0 0 100.00 0 Canceled
4 INN00005 2 0 1 1 Not Selected 0 Room_Type 1 48 2018 4 11 Online 0 0 0 94.50 0 Canceled
5 INN00006 2 0 0 2 Meal Plan 2 0 Room_Type 1 346 2018 9 13 Online 0 0 0 115.00 1 Canceled
6 INN00007 2 0 1 3 Meal Plan 1 0 Room_Type 1 34 2017 10 15 Online 0 0 0 107.55 1 Not_Canceled
7 INN00008 2 0 1 3 Meal Plan 1 0 Room_Type 4 83 2018 12 26 Online 0 0 0 105.61 1 Not_Canceled
8 INN00009 3 0 0 4 Meal Plan 1 0 Room_Type 1 121 2018 7 6 Offline 0 0 0 96.90 1 Not_Canceled
9 INN00010 2 0 0 5 Meal Plan 1 0 Room_Type 4 44 2018 10 18 Online 0 0 0 133.44 3 Not_Canceled

Data Overview¶

  • Observations
  • Sanity checks
In [3]:
# Checking for duplicate values
df.duplicated().sum()
Out[3]:
0
In [4]:
df.shape  # Displays the rows and columns of the dataset respectively
Out[4]:
(36275, 19)
In [5]:
df.info()  # Displays the datatypes of the dataset

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36275 entries, 0 to 36274
Data columns (total 19 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   Booking_ID                            36275 non-null  object 
 1   no_of_adults                          36275 non-null  int64  
 2   no_of_children                        36275 non-null  int64  
 3   no_of_weekend_nights                  36275 non-null  int64  
 4   no_of_week_nights                     36275 non-null  int64  
 5   type_of_meal_plan                     36275 non-null  object 
 6   required_car_parking_space            36275 non-null  int64  
 7   room_type_reserved                    36275 non-null  object 
 8   lead_time                             36275 non-null  int64  
 9   arrival_year                          36275 non-null  int64  
 10  arrival_month                         36275 non-null  int64  
 11  arrival_date                          36275 non-null  int64  
 12  market_segment_type                   36275 non-null  object 
 13  repeated_guest                        36275 non-null  int64  
 14  no_of_previous_cancellations          36275 non-null  int64  
 15  no_of_previous_bookings_not_canceled  36275 non-null  int64  
 16  avg_price_per_room                    36275 non-null  float64
 17  no_of_special_requests                36275 non-null  int64  
 18  booking_status                        36275 non-null  object 
dtypes: float64(1), int64(13), object(5)
memory usage: 5.3+ MB
In [6]:
df.isnull().sum()  # Checking for null values
Out[6]:
Booking_ID                              0
no_of_adults                            0
no_of_children                          0
no_of_weekend_nights                    0
no_of_week_nights                       0
type_of_meal_plan                       0
required_car_parking_space              0
room_type_reserved                      0
lead_time                               0
arrival_year                            0
arrival_month                           0
arrival_date                            0
market_segment_type                     0
repeated_guest                          0
no_of_previous_cancellations            0
no_of_previous_bookings_not_canceled    0
avg_price_per_room                      0
no_of_special_requests                  0
booking_status                          0
dtype: int64
In [7]:
df.describe()  # Displays Statistical Summary of the Data
Out[7]:
no_of_adults no_of_children no_of_weekend_nights no_of_week_nights required_car_parking_space lead_time arrival_year arrival_month arrival_date repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests
count 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000
mean 1.844962 0.105279 0.810724 2.204300 0.030986 85.232557 2017.820427 7.423653 15.596995 0.025637 0.023349 0.153411 103.423539 0.619655
std 0.518715 0.402648 0.870644 1.410905 0.173281 85.930817 0.383836 3.069894 8.740447 0.158053 0.368331 1.754171 35.089424 0.786236
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2017.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 0.000000 1.000000 0.000000 17.000000 2018.000000 5.000000 8.000000 0.000000 0.000000 0.000000 80.300000 0.000000
50% 2.000000 0.000000 1.000000 2.000000 0.000000 57.000000 2018.000000 8.000000 16.000000 0.000000 0.000000 0.000000 99.450000 0.000000
75% 2.000000 0.000000 2.000000 3.000000 0.000000 126.000000 2018.000000 10.000000 23.000000 0.000000 0.000000 0.000000 120.000000 1.000000
max 4.000000 10.000000 7.000000 17.000000 1.000000 443.000000 2018.000000 12.000000 31.000000 1.000000 13.000000 58.000000 540.000000 5.000000

Section Summary:¶

  • There are 36275 rows and 19 columns
  • All the datatypes for each column seem appropriate and nothing needs to be converted (repeated_guest uses 0/1 but is classified as an integer instead of boolean however, it does not need converting as it will not affect our analysis for now)
  • There are no null or duplicate values
  • Refer to df.describe() for a direct statistical summary

Exploratory Data Analysis (EDA)¶

  • EDA is an important part of any project involving data.
  • It is important to investigate and understand the data better before building a model with it.
  • A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
  • A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Leading Questions:

  1. What are the busiest months in the hotel?
  2. Which market segment do most of the guests come from?
  3. Hotel rates are dynamic and change according to demand and customer demographics. What are the differences in room prices in different market segments?
  4. What percentage of bookings are canceled?
  5. Repeating guests are the guests who stay in the hotel often and are important to brand equity. What percentage of repeating guests cancel?
  6. Many guests have special requirements when booking a hotel room. Do these requirements affect booking cancellation?

[1] Busiest Months in the hotel¶

In [8]:
# Creating a countplot of each month
plt.figure(figsize=(15, 5))
fig1 = sns.countplot(data=df, x="arrival_month")
plt.grid(axis="y", linewidth=0.5)
fig1.bar_label(fig1.containers[0], label_type="edge")
# Add data labels
Out[8]:
[Text(0, 0, '1014'),
 Text(0, 0, '1704'),
 Text(0, 0, '2358'),
 Text(0, 0, '2736'),
 Text(0, 0, '2598'),
 Text(0, 0, '3203'),
 Text(0, 0, '2920'),
 Text(0, 0, '3813'),
 Text(0, 0, '4611'),
 Text(0, 0, '5317'),
 Text(0, 0, '2980'),
 Text(0, 0, '3021')]

The three busiest months are October [5317 bookings], September [4611 bookings] and August [3813 bookings]¶

[2] Market Segment analysis¶

In [9]:
# Creating a count plot of the different market segments
plt.figure(figsize=(15, 10))
fig2 = sns.countplot(data=df, x="market_segment_type", palette="mako")
plt.grid(axis="y", linewidth=0.5)
fig2.bar_label(fig2.containers[0], label_type="edge")
# Add data labels
Out[9]:
[Text(0, 0, '10528'),
 Text(0, 0, '23214'),
 Text(0, 0, '2017'),
 Text(0, 0, '125'),
 Text(0, 0, '391')]

Most of the Market is Online with over 23200 bookings, with the second largest market segmentation being Offline with less than half the bookings of online (10528)¶

[3] Room Prices based on market segments¶

In [10]:
# Creating a Barplot of Median Room Price vs. Market Segment Type
fig3 = (
    df.groupby("market_segment_type")["avg_price_per_room"]
    .median(numeric_only=True)
    .plot.bar(color="purple")
)
plt.grid(axis="y", linewidth=0.5)
plt.ylabel("Average Price per Room (in €)")
fig3.bar_label(fig3.containers[0], label_type="edge")
# Add data labels
Out[10]:
[Text(0, 0, '95'),
 Text(0, 0, '0'),
 Text(0, 0, '79'),
 Text(0, 0, '90'),
 Text(0, 0, '107.1')]

Taking the median room prices for each market segment type, the highest median room price is for the Online market at 107.1€ and the lowest is Complimentary which is, as expected 0€ median per room, however the mean for Complimentary market is >0€ which we will analyse further¶

[4] Percentage of cancelled bookings¶

In [11]:
# Calculating percentage (No of canceled bookings / Total bookings * 100)

df[df["booking_status"] == "Canceled"].shape[0] / df.shape[0] * 100
Out[11]:
32.76361130254997

32.76% of all hotel bookings in the dataset have been canceled¶

[5] Repeat guest booking cancellation analysis¶

In [12]:
# Create a grouped dataset filtering the decimal percentage of cancelled/non-cancelled bookings by repeat/non-repeat guests
print(df.groupby("repeated_guest")["booking_status"].value_counts(1))

# As the cancelled booking decimal % for repeated guests is 0.0172, we know that only 1.72% of bookings are cancelled by repeat guests

repeated_guest  booking_status
0               Not_Canceled      0.664196
                Canceled          0.335804
1               Not_Canceled      0.982796
                Canceled          0.017204
Name: booking_status, dtype: float64

Only about 1.72% of hotel bookings are canceled by repeat guests, that is over 19 times less likely than the average percentage of hotel booking cencellations¶

[6] Do special requirements by guests affect booking cancellations¶

In [13]:
df.head(2)
Out[13]:
Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests booking_status
0 INN00001 2 0 1 2 Meal Plan 1 0 Room_Type 1 224 2017 10 2 Offline 0 0 0 65.00 0 Not_Canceled
1 INN00002 2 0 2 3 Not Selected 0 Room_Type 1 5 2018 11 6 Online 0 0 0 106.68 1 Not_Canceled
In [14]:
# Creating a countplot of booking status based on number of special requests
plt.figure(figsize=(15, 9))
fig4 = sns.countplot(
    data=df, x="no_of_special_requests", hue="booking_status", palette="mako"
)
plt.grid(axis="y", linewidth=0.5)
In [15]:
# Creating a countplot of booking status based on if there was a special request
df["special_request"] = df["no_of_special_requests"] > 0
fig5 = sns.countplot(
    data=df, x="special_request", hue="booking_status", palette="rocket"
)

Guests with special requests are more likely to not cancel their bookings than guests without a special request. As the number of special requests increases, the likelyhood of the guest cancelling their booking also decreases¶

Section Summary:¶

-

Data Preprocessing¶

  • Missing value treatment (if needed)
  • Feature engineering (if needed)
  • Outlier detection and treatment (if needed)
  • Preparing data for modeling
  • Any other preprocessing steps (if needed)
In [16]:
# Checking for outliers
df.describe()
Out[16]:
no_of_adults no_of_children no_of_weekend_nights no_of_week_nights required_car_parking_space lead_time arrival_year arrival_month arrival_date repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests
count 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000 36275.000000
mean 1.844962 0.105279 0.810724 2.204300 0.030986 85.232557 2017.820427 7.423653 15.596995 0.025637 0.023349 0.153411 103.423539 0.619655
std 0.518715 0.402648 0.870644 1.410905 0.173281 85.930817 0.383836 3.069894 8.740447 0.158053 0.368331 1.754171 35.089424 0.786236
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 2017.000000 1.000000 1.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 2.000000 0.000000 0.000000 1.000000 0.000000 17.000000 2018.000000 5.000000 8.000000 0.000000 0.000000 0.000000 80.300000 0.000000
50% 2.000000 0.000000 1.000000 2.000000 0.000000 57.000000 2018.000000 8.000000 16.000000 0.000000 0.000000 0.000000 99.450000 0.000000
75% 2.000000 0.000000 2.000000 3.000000 0.000000 126.000000 2018.000000 10.000000 23.000000 0.000000 0.000000 0.000000 120.000000 1.000000
max 4.000000 10.000000 7.000000 17.000000 1.000000 443.000000 2018.000000 12.000000 31.000000 1.000000 13.000000 58.000000 540.000000 5.000000

Number of previous cancellations seems to have a high maximum outlier¶

In [17]:
# Create a boxplot of number of previoous cancellations
fig6 = sns.boxplot(data=df, y="no_of_previous_cancellations")
In [18]:
# Create a countplot of number of previous cancellations
fig7 = sns.countplot(
    data=df[df["no_of_previous_cancellations"] > 0],
    x="no_of_previous_cancellations",
    palette="rocket",
)
fig7.bar_label(fig7.containers[0], label_type="edge")
# Add data labels
df.shape  # Check shape of dataset again
Out[18]:
(36275, 20)

While the number of previous cancellations seem insignificant after 3, having 25 individual bookings where the previous number of cancellations were 11 seem quite high, let us explore this variable further¶

In [19]:
# Filtering the dataset to show the number of bookings with more than 10 previous cancellations
df[df["no_of_previous_cancellations"] > 10]
Out[19]:
Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests booking_status special_request
1561 INN01562 2 0 0 1 Meal Plan 1 0 Room_Type 1 0 2018 1 14 Online 1 11 4 81.90 0 Not_Canceled False
3322 INN03323 2 0 0 1 Meal Plan 1 0 Room_Type 4 5 2018 1 15 Online 1 11 5 89.30 1 Not_Canceled True
3530 INN03531 2 0 0 1 Not Selected 1 Room_Type 1 2 2018 1 16 Online 1 11 10 86.00 1 Not_Canceled True
7110 INN07111 2 0 0 1 Not Selected 0 Room_Type 1 10 2018 2 13 Online 1 11 22 79.00 1 Not_Canceled True
10686 INN10687 2 0 0 2 Not Selected 0 Room_Type 1 1 2018 2 12 Online 1 11 22 79.00 1 Not_Canceled True
10890 INN10891 2 0 0 1 Not Selected 0 Room_Type 1 1 2018 1 15 Online 1 11 5 75.00 0 Not_Canceled False
11834 INN11835 2 0 0 2 Not Selected 1 Room_Type 1 1 2018 1 8 Online 1 11 0 76.50 0 Not_Canceled False
12097 INN12098 2 0 1 1 Meal Plan 1 0 Room_Type 1 190 2018 4 9 Offline 1 13 1 70.00 0 Canceled False
12109 INN12110 2 0 1 1 Meal Plan 1 0 Room_Type 1 190 2018 4 9 Offline 1 13 1 70.00 0 Canceled False
12554 INN12555 2 0 0 1 Meal Plan 1 0 Room_Type 1 3 2018 1 14 Online 1 11 4 83.90 1 Not_Canceled True
14030 INN14031 2 0 0 2 Not Selected 0 Room_Type 1 1 2018 1 8 Online 1 11 0 67.50 0 Not_Canceled False
16277 INN16278 2 0 1 1 Meal Plan 1 0 Room_Type 1 190 2018 4 9 Offline 1 13 1 70.00 0 Canceled False
16919 INN16920 1 0 0 2 Meal Plan 1 0 Room_Type 1 2 2018 1 12 Online 1 11 4 82.90 1 Not_Canceled True
19779 INN19780 3 0 0 3 Meal Plan 1 0 Room_Type 4 8 2018 2 10 Online 1 11 22 92.67 2 Not_Canceled True
20739 INN20740 1 0 0 1 Meal Plan 1 0 Room_Type 1 1 2018 1 14 Online 1 11 4 67.22 0 Not_Canceled False
23792 INN23793 1 0 0 2 Meal Plan 1 0 Room_Type 1 5 2018 1 14 Online 1 11 4 73.90 1 Not_Canceled True
24950 INN24951 2 0 1 0 Meal Plan 1 0 Room_Type 1 0 2018 1 17 Online 1 11 16 93.00 0 Not_Canceled False
27499 INN27500 2 0 1 1 Meal Plan 1 0 Room_Type 4 1 2018 1 9 Online 1 11 1 89.30 1 Not_Canceled True
28891 INN28892 2 0 0 1 Not Selected 0 Room_Type 1 4 2018 1 15 Online 1 11 5 77.00 1 Not_Canceled True
28914 INN28915 2 0 2 2 Meal Plan 1 0 Room_Type 4 1 2018 1 29 Online 1 11 20 106.00 1 Not_Canceled True
28972 INN28973 1 0 0 1 Meal Plan 1 0 Room_Type 1 4 2018 1 15 Online 1 11 5 73.90 1 Not_Canceled True
30363 INN30364 2 0 1 2 Meal Plan 1 0 Room_Type 1 5 2018 1 22 Online 1 11 19 89.00 1 Not_Canceled True
30833 INN30834 2 0 1 1 Meal Plan 1 0 Room_Type 1 190 2018 4 9 Offline 1 13 1 70.00 0 Canceled False
32148 INN32149 1 0 0 2 Meal Plan 1 0 Room_Type 1 2 2018 1 13 Online 1 11 4 77.50 0 Not_Canceled False
32722 INN32723 2 0 1 2 Meal Plan 1 0 Room_Type 4 12 2018 2 5 Online 1 11 21 108.00 1 Not_Canceled True
33760 INN33761 2 0 0 1 Meal Plan 1 0 Room_Type 1 8 2018 1 15 Online 1 11 5 80.30 1 Not_Canceled True
34906 INN34907 2 0 0 3 Meal Plan 1 0 Room_Type 1 4 2018 1 12 Online 1 11 4 80.30 1 Not_Canceled True
34909 INN34910 2 0 2 2 Meal Plan 1 0 Room_Type 1 3 2018 1 16 Online 1 11 10 80.10 1 Not_Canceled True
36079 INN36080 1 0 0 1 Not Selected 1 Room_Type 1 0 2018 1 7 Online 1 11 0 77.50 0 Not_Canceled False

Out of the people that have cancelled more than 10 times, only a few of them have previously booked the hotel without cancelling more than ones that booked and cancelled their stays. The total number of bookings in the dataset are 36275, these 29 customers with significantly more cancellations than the majority can be looked at as outliers and be removed from the dataset to ensure a more robust model¶

In [20]:
# Removing the outliers from the dataset
df = df[df["no_of_previous_cancellations"] < 10]
df.shape  # Checking shape
Out[20]:
(36246, 20)

Preparing data for modelling¶

Creating Dummy Variables¶

In [189]:
# Creating a Duplicate dataframe for regression model and Dropping Booking_ID and Special Request Columns
df_reg = df
df_reg = df_reg.drop(["special_request"], axis=1)
df_reg = df_reg.drop(["Booking_ID"], axis=1)
print(df_reg.shape)


df_reg = pd.get_dummies(df_reg, columns=["type_of_meal_plan"], drop_first=True)
df_reg = pd.get_dummies(df_reg, columns=["room_type_reserved"], drop_first=True)
df_reg = pd.get_dummies(df_reg, columns=["market_segment_type"], drop_first=True)

df_reg.shape

(36246, 18)
Out[189]:
(36246, 28)
In [190]:
# Replacing booking status strings with either 0 or 1 integers
df_reg = df_reg.replace("Not_Canceled", 0)
df_reg = df_reg.replace("Canceled", 1)
df_reg.info()  # Displays all datatypes

<class 'pandas.core.frame.DataFrame'>
Int64Index: 36246 entries, 0 to 36274
Data columns (total 28 columns):
 #   Column                                Non-Null Count  Dtype  
---  ------                                --------------  -----  
 0   no_of_adults                          36246 non-null  int64  
 1   no_of_children                        36246 non-null  int64  
 2   no_of_weekend_nights                  36246 non-null  int64  
 3   no_of_week_nights                     36246 non-null  int64  
 4   required_car_parking_space            36246 non-null  int64  
 5   lead_time                             36246 non-null  int64  
 6   arrival_year                          36246 non-null  int64  
 7   arrival_month                         36246 non-null  int64  
 8   arrival_date                          36246 non-null  int64  
 9   repeated_guest                        36246 non-null  int64  
 10  no_of_previous_cancellations          36246 non-null  int64  
 11  no_of_previous_bookings_not_canceled  36246 non-null  int64  
 12  avg_price_per_room                    36246 non-null  float64
 13  no_of_special_requests                36246 non-null  int64  
 14  booking_status                        36246 non-null  int64  
 15  type_of_meal_plan_Meal Plan 2         36246 non-null  uint8  
 16  type_of_meal_plan_Meal Plan 3         36246 non-null  uint8  
 17  type_of_meal_plan_Not Selected        36246 non-null  uint8  
 18  room_type_reserved_Room_Type 2        36246 non-null  uint8  
 19  room_type_reserved_Room_Type 3        36246 non-null  uint8  
 20  room_type_reserved_Room_Type 4        36246 non-null  uint8  
 21  room_type_reserved_Room_Type 5        36246 non-null  uint8  
 22  room_type_reserved_Room_Type 6        36246 non-null  uint8  
 23  room_type_reserved_Room_Type 7        36246 non-null  uint8  
 24  market_segment_type_Complementary     36246 non-null  uint8  
 25  market_segment_type_Corporate         36246 non-null  uint8  
 26  market_segment_type_Offline           36246 non-null  uint8  
 27  market_segment_type_Online            36246 non-null  uint8  
dtypes: float64(1), int64(14), uint8(13)
memory usage: 4.9 MB

Splitting Data into Train and Test Set¶

In [191]:
X = df_reg.drop(["booking_status"], axis=1)
Y = df_reg["booking_status"]

# adding a contstant to X variable
X = add_constant(X)

# creating dummies
X = pd.get_dummies(X, drop_first=True)


# Splitting data in train and test sets
X_train, X_test, y_train, y_test = train_test_split(
    X, Y, test_size=0.30, random_state=1, stratify=Y
)

Section Summary:¶

  • There were no null or duplicate values in the dataset that needed to be filled/removed
  • Through statistical summary and graphical analysis, we have determined 29 rows to be outliers and removed them from the dataset
  • As there were several categorical variables in our dataset, we split them into dummy variables to be used in our regression modelling
  • We split the dataset into training and testing data for our regression model

EDA¶

  • It is a good idea to explore the data once again after manipulating it.
In [192]:
# Checking to see if the dummy variables have been created properly, there will only be 2 unique values (1/0) in each dummy variable
df_reg.nunique()
Out[192]:
no_of_adults                               5
no_of_children                             6
no_of_weekend_nights                       8
no_of_week_nights                         18
required_car_parking_space                 2
lead_time                                352
arrival_year                               2
arrival_month                             12
arrival_date                              31
repeated_guest                             2
no_of_previous_cancellations               7
no_of_previous_bookings_not_canceled      59
avg_price_per_room                      3930
no_of_special_requests                     6
booking_status                             2
type_of_meal_plan_Meal Plan 2              2
type_of_meal_plan_Meal Plan 3              2
type_of_meal_plan_Not Selected             2
room_type_reserved_Room_Type 2             2
room_type_reserved_Room_Type 3             2
room_type_reserved_Room_Type 4             2
room_type_reserved_Room_Type 5             2
room_type_reserved_Room_Type 6             2
room_type_reserved_Room_Type 7             2
market_segment_type_Complementary          2
market_segment_type_Corporate              2
market_segment_type_Offline                2
market_segment_type_Online                 2
dtype: int64
  • All dummy variables were created properly as there are only 2 unique values
In [194]:
# Output the Rows and Columns of the dataset respectively
df_reg.shape
Out[194]:
(36246, 28)
  • There are now 28 columns in this dataset due to the creation of the dummy variables
In [202]:
# Checking statistical summary of altered dataset after removing outliers
df_reg["no_of_previous_cancellations"].describe()
Out[202]:
count    36246.000000
mean         0.014346
std          0.184407
min          0.000000
25%          0.000000
50%          0.000000
75%          0.000000
max          6.000000
Name: no_of_previous_cancellations, dtype: float64
  • Upon removing the outliers, our maximum value and std deviation for this column has halved

Creating a Heatmap¶

In [207]:
# Making a duplicate dataset and replacing strings with binary values to show direct correlation trends in heatmap
df_hm = df.replace("Not_Canceled", 0)
df_hm = df_hm.replace("Canceled", 1)

df_hm
Out[207]:
Booking_ID no_of_adults no_of_children no_of_weekend_nights no_of_week_nights type_of_meal_plan required_car_parking_space room_type_reserved lead_time arrival_year arrival_month arrival_date market_segment_type repeated_guest no_of_previous_cancellations no_of_previous_bookings_not_canceled avg_price_per_room no_of_special_requests booking_status special_request
0 INN00001 2 0 1 2 Meal Plan 1 0 Room_Type 1 224 2017 10 2 Offline 0 0 0 65.00 0 0 False
1 INN00002 2 0 2 3 Not Selected 0 Room_Type 1 5 2018 11 6 Online 0 0 0 106.68 1 0 True
2 INN00003 1 0 2 1 Meal Plan 1 0 Room_Type 1 1 2018 2 28 Online 0 0 0 60.00 0 1 False
3 INN00004 2 0 0 2 Meal Plan 1 0 Room_Type 1 211 2018 5 20 Online 0 0 0 100.00 0 1 False
4 INN00005 2 0 1 1 Not Selected 0 Room_Type 1 48 2018 4 11 Online 0 0 0 94.50 0 1 False
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
36270 INN36271 3 0 2 6 Meal Plan 1 0 Room_Type 4 85 2018 8 3 Online 0 0 0 167.80 1 0 True
36271 INN36272 2 0 1 3 Meal Plan 1 0 Room_Type 1 228 2018 10 17 Online 0 0 0 90.95 2 1 True
36272 INN36273 2 0 2 6 Meal Plan 1 0 Room_Type 1 148 2018 7 1 Online 0 0 0 98.39 2 0 True
36273 INN36274 2 0 0 3 Not Selected 0 Room_Type 1 63 2018 4 21 Online 0 0 0 94.50 0 1 False
36274 INN36275 2 0 1 2 Meal Plan 1 0 Room_Type 1 207 2018 12 30 Offline 0 0 0 161.67 0 0 False

36246 rows × 20 columns

In [216]:
# Creating a Heatmap
plt.figure(figsize=(17, 12))
sns.heatmap(df_hm.corr(numeric_only=True), annot=True, cmap="Spectral", vmin=-1, vmax=1)
plt.show()
  • From the heatmap, we can see that lead time has the highest positive correlation with booking status, with special requests and arrival year having a minor negative and positive correlation with booking status respectively

Section Summary:¶

  • The dummy variables have been created successfully and our regression model dataset now consists of 36246 rows and 28 columns
  • The removal of outliers resulted in half the maximum value and standard deviation for our targeted column
  • The heatmap shows a strong relation between lead time and booking status alonmg with a few minor correlations between if the guests have any special requests, if the booking was made by a repeated guest and the arrival year, these will be further explored in our model

Checking Multicollinearity¶

  • In order to make statistical inferences from a logistic regression model, it is important to ensure that there is no multicollinearity present in the data.
In [137]:
# let's check the VIF of the predictors
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                   3.946254e+07
no_of_adults                            1.340447e+00
no_of_children                          1.924150e+00
no_of_weekend_nights                    1.070664e+00
no_of_week_nights                       1.097704e+00
required_car_parking_space              1.036525e+00
lead_time                               1.393533e+00
arrival_year                            1.437135e+00
arrival_month                           1.270359e+00
arrival_date                            1.007380e+00
repeated_guest                          1.719460e+00
no_of_previous_cancellations            2.180645e+00
no_of_previous_bookings_not_canceled    2.344258e+00
avg_price_per_room                      2.036414e+00
no_of_special_requests                  1.250788e+00
type_of_meal_plan_Meal Plan 2           1.265350e+00
type_of_meal_plan_Meal Plan 3           1.025209e+00
type_of_meal_plan_Not Selected          1.275806e+00
room_type_reserved_Room_Type 2          1.092112e+00
room_type_reserved_Room_Type 3          1.001197e+00
room_type_reserved_Room_Type 4          1.363580e+00
room_type_reserved_Room_Type 5          1.031790e+00
room_type_reserved_Room_Type 6          1.931982e+00
room_type_reserved_Room_Type 7          1.123504e+00
market_segment_type_Complementary       4.189398e+00
market_segment_type_Corporate           1.535340e+01
market_segment_type_Offline             5.783434e+01
market_segment_type_Online              6.408100e+01
dtype: float64

Market Segments have very high multicollinearity, let us try removing market_segment_type_Online as it has the highest multicollinearity and see how it affects our VIF values¶

In [138]:
# Dropping market_segment_type_Online from the training data
X_train = X_train.drop(["market_segment_type_Online"], axis=1)

# let's check the VIF of the predictors again
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                   3.937402e+07
no_of_adults                            1.322499e+00
no_of_children                          1.923254e+00
no_of_weekend_nights                    1.070151e+00
no_of_week_nights                       1.096666e+00
required_car_parking_space              1.036525e+00
lead_time                               1.388523e+00
arrival_year                            1.434248e+00
arrival_month                           1.269288e+00
arrival_date                            1.007372e+00
repeated_guest                          1.717102e+00
no_of_previous_cancellations            2.180610e+00
no_of_previous_bookings_not_canceled    2.343721e+00
avg_price_per_room                      2.035541e+00
no_of_special_requests                  1.245677e+00
type_of_meal_plan_Meal Plan 2           1.264939e+00
type_of_meal_plan_Meal Plan 3           1.025209e+00
type_of_meal_plan_Not Selected          1.273682e+00
room_type_reserved_Room_Type 2          1.091935e+00
room_type_reserved_Room_Type 3          1.001197e+00
room_type_reserved_Room_Type 4          1.358342e+00
room_type_reserved_Room_Type 5          1.031790e+00
room_type_reserved_Room_Type 6          1.931657e+00
room_type_reserved_Room_Type 7          1.123356e+00
market_segment_type_Complementary       1.360896e+00
market_segment_type_Corporate           1.519875e+00
market_segment_type_Offline             1.610215e+00
dtype: float64
  • While the VIF values look alright, running the model now returns an error, this is resolved by removing 2 additional columns that displayed high relative multicollinearity
In [139]:
# Dropping market_segment_type_Online from the training data
X_train = X_train.drop(["market_segment_type_Offline"], axis=1)

# let's check the VIF of the predictors again
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                   3.923399e+07
no_of_adults                            1.322200e+00
no_of_children                          1.919913e+00
no_of_weekend_nights                    1.064620e+00
no_of_week_nights                       1.094575e+00
required_car_parking_space              1.035138e+00
lead_time                               1.366900e+00
arrival_year                            1.429207e+00
arrival_month                           1.268399e+00
arrival_date                            1.007356e+00
repeated_guest                          1.716802e+00
no_of_previous_cancellations            2.180419e+00
no_of_previous_bookings_not_canceled    2.343211e+00
avg_price_per_room                      1.947482e+00
no_of_special_requests                  1.145572e+00
type_of_meal_plan_Meal Plan 2           1.205473e+00
type_of_meal_plan_Meal Plan 3           1.025185e+00
type_of_meal_plan_Not Selected          1.173885e+00
room_type_reserved_Room_Type 2          1.080601e+00
room_type_reserved_Room_Type 3          1.001191e+00
room_type_reserved_Room_Type 4          1.336218e+00
room_type_reserved_Room_Type 5          1.030384e+00
room_type_reserved_Room_Type 6          1.931338e+00
room_type_reserved_Room_Type 7          1.122559e+00
market_segment_type_Complementary       1.326263e+00
market_segment_type_Corporate           1.419882e+00
dtype: float64
In [140]:
# Dropping market_segment_type_Online from the training data
X_train = X_train.drop(["no_of_previous_bookings_not_canceled"], axis=1)

# let's check the VIF of the predictors again
vif_series = pd.Series(
    [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])],
    index=X_train.columns,
    dtype=float,
)
print("VIF values: \n\n{}\n".format(vif_series))

VIF values: 

const                                3.905665e+07
no_of_adults                         1.321707e+00
no_of_children                       1.919873e+00
no_of_weekend_nights                 1.064568e+00
no_of_week_nights                    1.094574e+00
required_car_parking_space           1.034809e+00
lead_time                            1.366892e+00
arrival_year                         1.422746e+00
arrival_month                        1.268399e+00
arrival_date                         1.007197e+00
repeated_guest                       1.632303e+00
no_of_previous_cancellations         1.303820e+00
avg_price_per_room                   1.946136e+00
no_of_special_requests               1.139100e+00
type_of_meal_plan_Meal Plan 2        1.204575e+00
type_of_meal_plan_Meal Plan 3        1.025158e+00
type_of_meal_plan_Not Selected       1.173859e+00
room_type_reserved_Room_Type 2       1.080598e+00
room_type_reserved_Room_Type 3       1.001188e+00
room_type_reserved_Room_Type 4       1.336105e+00
room_type_reserved_Room_Type 5       1.030096e+00
room_type_reserved_Room_Type 6       1.931033e+00
room_type_reserved_Room_Type 7       1.121675e+00
market_segment_type_Complementary    1.320492e+00
market_segment_type_Corporate        1.408832e+00
dtype: float64

All VIF Values are now under 2 which indicates little to no multicollinearity, we can proceed with creating the model¶

Building a Logistic Regression model¶

In [141]:
# fitting the model on training set
logit = sm.Logit(y_train, X_train.astype(float))
In [142]:
lg = logit.fit(maxiter=200)

Warning: Maximum number of iterations has been exceeded.
         Current function value: 0.452066
         Iterations: 200

C:\Users\yatik\anaconda3\lib\site-packages\statsmodels\base\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals
  warnings.warn("Maximum Likelihood optimization failed to "
In [143]:
# let's print the logistic regression summary
print(lg.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25372
Model:                          Logit   Df Residuals:                    25347
Method:                           MLE   Df Model:                           24
Date:                Fri, 23 Feb 2024   Pseudo R-squ.:                  0.2854
Time:                        17:26:06   Log-Likelihood:                -11470.
converged:                      False   LL-Null:                       -16051.
Covariance Type:            nonrobust   LLR p-value:                     0.000
=====================================================================================================
                                        coef    std err          z      P>|z|      [0.025      0.975]
-----------------------------------------------------------------------------------------------------
const                             -1134.0547    117.655     -9.639      0.000   -1364.654    -903.456
no_of_adults                          0.1159      0.036      3.238      0.001       0.046       0.186
no_of_children                        0.2320      0.054      4.278      0.000       0.126       0.338
no_of_weekend_nights                  0.1715      0.019      9.003      0.000       0.134       0.209
no_of_week_nights                     0.0757      0.012      6.392      0.000       0.052       0.099
required_car_parking_space           -1.2532      0.132     -9.486      0.000      -1.512      -0.994
lead_time                             0.0134      0.000     55.586      0.000       0.013       0.014
arrival_year                          0.5601      0.058      9.605      0.000       0.446       0.674
arrival_month                        -0.0496      0.006     -7.889      0.000      -0.062      -0.037
arrival_date                          0.0028      0.002      1.481      0.139      -0.001       0.006
repeated_guest                       -2.8340      0.627     -4.520      0.000      -4.063      -1.605
no_of_previous_cancellations          0.4037      0.426      0.948      0.343      -0.431       1.238
avg_price_per_room                    0.0221      0.001     30.786      0.000       0.021       0.023
no_of_special_requests               -1.1795      0.027    -43.183      0.000      -1.233      -1.126
type_of_meal_plan_Meal Plan 2        -0.3495      0.062     -5.623      0.000      -0.471      -0.228
type_of_meal_plan_Meal Plan 3        26.7609   8.24e+05   3.25e-05      1.000   -1.61e+06    1.61e+06
type_of_meal_plan_Not Selected        0.7610      0.050     15.369      0.000       0.664       0.858
room_type_reserved_Room_Type 2        0.2078      0.125      1.667      0.096      -0.037       0.452
room_type_reserved_Room_Type 3       -0.0536      1.115     -0.048      0.962      -2.238       2.131
room_type_reserved_Room_Type 4        0.0585      0.050      1.161      0.246      -0.040       0.157
room_type_reserved_Room_Type 5       -1.0446      0.209     -5.008      0.000      -1.453      -0.636
room_type_reserved_Room_Type 6       -0.9570      0.141     -6.769      0.000      -1.234      -0.680
room_type_reserved_Room_Type 7       -1.6868      0.311     -5.419      0.000      -2.297      -1.077
market_segment_type_Complementary   -33.2764   8.24e+05  -4.04e-05      1.000   -1.61e+06    1.61e+06
market_segment_type_Corporate        -0.0773      0.099     -0.777      0.437      -0.272       0.118
=====================================================================================================
In [149]:
# Checking Accuracy
pred_train = lg.predict(X_train) > 0.5
pred_train = np.round(pred_train)
print("Accuracy on training set : ", accuracy_score(y_train, pred_train))

Accuracy on training set :  0.7880734668138105

Removing insignificant variables (p-value > 0.05 )¶

In [145]:
# [1] Removing variables with high P values
X_train1 = X_train.drop(["market_segment_type_Complementary"], axis=1)
In [147]:
# Fitting the model after removing the variable
logit1 = sm.Logit(y_train, X_train1.astype(float))
lg1 = logit1.fit()

# Checking Accuracy
pred_train1 = lg1.predict(X_train1)
pred_train1 = np.round(pred_train1)

print("Accuracy on training set : ", accuracy_score(y_train, pred_train1))

Optimization terminated successfully.
         Current function value: 0.452297
         Iterations 12
Accuracy on training set :  0.7880340532870881
In [151]:
# let's print the logistic regression summary
print(lg1.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25372
Model:                          Logit   Df Residuals:                    25348
Method:                           MLE   Df Model:                           23
Date:                Fri, 23 Feb 2024   Pseudo R-squ.:                  0.2850
Time:                        17:31:40   Log-Likelihood:                -11476.
converged:                       True   LL-Null:                       -16051.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                          -1123.8341    117.581     -9.558      0.000   -1354.289    -893.379
no_of_adults                       0.1171      0.036      3.272      0.001       0.047       0.187
no_of_children                     0.2307      0.054      4.255      0.000       0.124       0.337
no_of_weekend_nights               0.1717      0.019      9.015      0.000       0.134       0.209
no_of_week_nights                  0.0765      0.012      6.459      0.000       0.053       0.100
required_car_parking_space        -1.2567      0.132     -9.513      0.000      -1.516      -0.998
lead_time                          0.0134      0.000     55.743      0.000       0.013       0.014
arrival_year                       0.5550      0.058      9.524      0.000       0.441       0.669
arrival_month                     -0.0502      0.006     -7.987      0.000      -0.063      -0.038
arrival_date                       0.0027      0.002      1.453      0.146      -0.001       0.006
repeated_guest                    -2.8400      0.626     -4.534      0.000      -4.067      -1.612
no_of_previous_cancellations       0.4059      0.425      0.955      0.340      -0.427       1.239
avg_price_per_room                 0.0223      0.001     31.269      0.000       0.021       0.024
no_of_special_requests            -1.1801      0.027    -43.207      0.000      -1.234      -1.127
type_of_meal_plan_Meal Plan 2     -0.3546      0.062     -5.706      0.000      -0.476      -0.233
type_of_meal_plan_Meal Plan 3      2.0237      2.695      0.751      0.453      -3.258       7.305
type_of_meal_plan_Not Selected     0.7645      0.050     15.444      0.000       0.667       0.862
room_type_reserved_Room_Type 2     0.2093      0.125      1.679      0.093      -0.035       0.454
room_type_reserved_Room_Type 3    -0.0737      1.105     -0.067      0.947      -2.239       2.092
room_type_reserved_Room_Type 4     0.0543      0.050      1.079      0.281      -0.044       0.153
room_type_reserved_Room_Type 5    -1.0516      0.209     -5.043      0.000      -1.460      -0.643
room_type_reserved_Room_Type 6    -0.9687      0.141     -6.856      0.000      -1.246      -0.692
room_type_reserved_Room_Type 7    -1.7108      0.311     -5.503      0.000      -2.320      -1.101
market_segment_type_Corporate     -0.0732      0.099     -0.735      0.462      -0.268       0.122
==================================================================================================
In [153]:
# [2] Removing variables with high P values
X_train2 = X_train1.drop(["market_segment_type_Corporate"], axis=1)
In [154]:
# fitting the model on training set
logit2 = sm.Logit(y_train, X_train2.astype(float))
lg2 = logit2.fit()

pred_train2 = lg2.predict(X_train2)
pred_train2 = np.round(pred_train2)

print("Accuracy on training set : ", accuracy_score(y_train, pred_train2))

Optimization terminated successfully.
         Current function value: 0.452308
         Iterations 12
Accuracy on training set :  0.7893346996689263
In [155]:
# let's print the logistic regression summary
print(lg2.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25372
Model:                          Logit   Df Residuals:                    25349
Method:                           MLE   Df Model:                           22
Date:                Fri, 23 Feb 2024   Pseudo R-squ.:                  0.2850
Time:                        17:33:24   Log-Likelihood:                -11476.
converged:                       True   LL-Null:                       -16051.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                          -1123.7810    117.566     -9.559      0.000   -1354.207    -893.355
no_of_adults                       0.1212      0.035      3.426      0.001       0.052       0.191
no_of_children                     0.2322      0.054      4.286      0.000       0.126       0.338
no_of_weekend_nights               0.1727      0.019      9.095      0.000       0.136       0.210
no_of_week_nights                  0.0768      0.012      6.485      0.000       0.054       0.100
required_car_parking_space        -1.2578      0.132     -9.520      0.000      -1.517      -0.999
lead_time                          0.0134      0.000     56.306      0.000       0.013       0.014
arrival_year                       0.5549      0.058      9.524      0.000       0.441       0.669
arrival_month                     -0.0502      0.006     -7.986      0.000      -0.063      -0.038
arrival_date                       0.0027      0.002      1.426      0.154      -0.001       0.006
repeated_guest                    -2.8658      0.622     -4.611      0.000      -4.084      -1.648
no_of_previous_cancellations       0.4019      0.421      0.953      0.340      -0.424       1.228
avg_price_per_room                 0.0223      0.001     31.334      0.000       0.021       0.024
no_of_special_requests            -1.1796      0.027    -43.190      0.000      -1.233      -1.126
type_of_meal_plan_Meal Plan 2     -0.3528      0.062     -5.680      0.000      -0.475      -0.231
type_of_meal_plan_Meal Plan 3      2.0312      2.699      0.753      0.452      -3.259       7.321
type_of_meal_plan_Not Selected     0.7687      0.049     15.630      0.000       0.672       0.865
room_type_reserved_Room_Type 2     0.2122      0.125      1.703      0.088      -0.032       0.456
room_type_reserved_Room_Type 3    -0.0777      1.102     -0.070      0.944      -2.238       2.083
room_type_reserved_Room_Type 4     0.0548      0.050      1.088      0.276      -0.044       0.153
room_type_reserved_Room_Type 5    -1.0679      0.208     -5.144      0.000      -1.475      -0.661
room_type_reserved_Room_Type 6    -0.9700      0.141     -6.865      0.000      -1.247      -0.693
room_type_reserved_Room_Type 7    -1.7144      0.311     -5.516      0.000      -2.324      -1.105
==================================================================================================

For the sake of brevity, this method was used 5 more times to remove the following columns with P>|z| values >0.05 one by one:¶

  • room_type_reserved_Room_Type 3
  • room_type_reserved_Room_Type 4
  • type_of_meal_plan_Meal Plan 3
  • no_of_previous_cancellations
  • arrival_date
  • room_type_reserved_Room_Type 2

Their respective impact on the accuracy of the model is as follows:¶

  • 0.7893346996689263
  • 0.7886646697146461
  • 0.7886252561879237
  • 0.7885070156077566
  • 0.7878369856534763
  • 0.7872457827526407

Below is the code showing these iterations grouped¶

In [180]:
# [3-8] Removing variables with high P values
X_trainF = X_train2.drop(
    [
        "room_type_reserved_Room_Type 2",
        "room_type_reserved_Room_Type 3",
        "room_type_reserved_Room_Type 4",
        "type_of_meal_plan_Meal Plan 3",
        "no_of_previous_cancellations",
        "arrival_date",
    ],
    axis=1,
)
In [181]:
# fitting the model on training set
logitF = sm.Logit(y_train, X_trainF.astype(float))
lgF = logitF.fit()

pred_trainF = lgF.predict(X_trainF)
pred_trainF = np.round(pred_trainF)

print("Accuracy on training set : ", accuracy_score(y_train, pred_trainF))

Optimization terminated successfully.
         Current function value: 0.452456
         Iterations 11
Accuracy on training set :  0.7872457827526407
In [182]:
## checking summary of the model
print(lg8.summary())

                           Logit Regression Results                           
==============================================================================
Dep. Variable:         booking_status   No. Observations:                25372
Model:                          Logit   Df Residuals:                    25355
Method:                           MLE   Df Model:                           16
Date:                Fri, 23 Feb 2024   Pseudo R-squ.:                  0.2848
Time:                        17:58:37   Log-Likelihood:                -11480.
converged:                       True   LL-Null:                       -16051.
Covariance Type:            nonrobust   LLR p-value:                     0.000
==================================================================================================
                                     coef    std err          z      P>|z|      [0.025      0.975]
--------------------------------------------------------------------------------------------------
const                          -1125.1263    117.459     -9.579      0.000   -1355.342    -894.911
no_of_adults                       0.1293      0.034      3.762      0.000       0.062       0.197
no_of_children                     0.2498      0.053      4.742      0.000       0.147       0.353
no_of_weekend_nights               0.1746      0.019      9.208      0.000       0.137       0.212
no_of_week_nights                  0.0780      0.012      6.631      0.000       0.055       0.101
required_car_parking_space        -1.2541      0.132     -9.509      0.000      -1.513      -0.996
lead_time                          0.0134      0.000     56.505      0.000       0.013       0.014
arrival_year                       0.5556      0.058      9.545      0.000       0.442       0.670
arrival_month                     -0.0511      0.006     -8.154      0.000      -0.063      -0.039
repeated_guest                    -2.5478      0.457     -5.580      0.000      -3.443      -1.653
avg_price_per_room                 0.0225      0.001     33.119      0.000       0.021       0.024
no_of_special_requests            -1.1762      0.027    -43.169      0.000      -1.230      -1.123
type_of_meal_plan_Meal Plan 2     -0.3637      0.062     -5.906      0.000      -0.484      -0.243
type_of_meal_plan_Not Selected     0.7541      0.048     15.725      0.000       0.660       0.848
room_type_reserved_Room_Type 5    -1.0882      0.207     -5.265      0.000      -1.493      -0.683
room_type_reserved_Room_Type 6    -1.0304      0.138     -7.475      0.000      -1.301      -0.760
room_type_reserved_Room_Type 7    -1.7674      0.309     -5.717      0.000      -2.373      -1.161
==================================================================================================

Model Performance Evaluation¶

In [186]:
cm = confusion_matrix(y_train, pred_train)
sns.heatmap(cm, annot=True, fmt="g")
plt.xlabel("Predicted Values")
plt.ylabel("Actual Values")
plt.show()
In [217]:
print("Accuracy on training set : ", accuracy_score(y_train, pred_trainF))

Accuracy on training set :  0.7872457827526407

ROC-AUC on training set

In [219]:
logit_roc_auc_train = roc_auc_score(y_train, lgF.predict(X_trainF))
fpr, tpr, thresholds = roc_curve(y_train, lgF.predict(X_trainF))
plt.figure(figsize=(5, 5))
plt.plot(fpr, tpr, label="Logistic Regression (area = %0.2f)" % logit_roc_auc_train)
plt.plot([0, 1], [0, 1], "r--")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.title("Receiver operating characteristic")
plt.legend(loc="lower right")
plt.show()

Odds from coefficients

In [220]:
# converting coefficients to odds
odds = np.exp(lgF.params)

# adding the odds to a dataframe
pd.DataFrame(odds, X_trainF.columns, columns=["odds"]).T
Out[220]:
const no_of_adults no_of_children no_of_weekend_nights no_of_week_nights required_car_parking_space lead_time arrival_year arrival_month repeated_guest avg_price_per_room no_of_special_requests type_of_meal_plan_Meal Plan 2 type_of_meal_plan_Not Selected room_type_reserved_Room_Type 5 room_type_reserved_Room_Type 6 room_type_reserved_Room_Type 7
odds 0.0 1.13802 1.283717 1.190757 1.081168 0.285318 1.013475 1.743028 0.950145 0.078257 1.022722 0.308439 0.695081 2.125619 0.33683 0.356856 0.170782
In [221]:
# finding the percentage change
perc_change_odds = (np.exp(lgF.params) - 1) * 100

# adding the change_odds% to a dataframe
pd.DataFrame(perc_change_odds, X_trainF.columns, columns=["change_odds%"]).T
Out[221]:
const no_of_adults no_of_children no_of_weekend_nights no_of_week_nights required_car_parking_space lead_time arrival_year arrival_month repeated_guest avg_price_per_room no_of_special_requests type_of_meal_plan_Meal Plan 2 type_of_meal_plan_Not Selected room_type_reserved_Room_Type 5 room_type_reserved_Room_Type 6 room_type_reserved_Room_Type 7
change_odds% -100.0 13.802024 28.371748 19.075744 8.116821 -71.46816 1.347539 74.302765 -4.985505 -92.174252 2.272203 -69.156143 -30.491853 112.561887 -66.316971 -64.314359 -82.921847

Coefficient to Odds Interpretation:¶

Looking closely at the main contributors that affect cancellation odds (>50%) and hassuming all other variables are held constant:

  • A unit increase in Required Car Parking Space will decrease the odds of the booking being cancelled by 71.47%
  • An increase in Arrival Year by each year will increase the odds of the booking being cancelled by 74.30%
  • A unit change in Repeated Guest will decrease the odds of the booking being cancelled by 92.17%
  • A unit change in Number of special requests will decrease the odds of the booking being cancelled by 69.16%
  • If the Meal plan is not selected it will increase the odds of the booking being cancelled by 112.56%
  • A unit change in Type 5 reserved room will decrease the odds of the booking being cancelled by 66.32%
  • A unit increase in Type 6 reserved room will decrease the odds of the booking being cancelled by 64.31%
  • A unit increase in Type 7 reserved room will decrease the odds of the booking being cancelled by 82.92%
In [224]:
# dropping variables from test set as well which were dropped from training set
X_test = X_test.drop(
    [
        "market_segment_type_Online",
        "market_segment_type_Offline",
        "no_of_previous_bookings_not_canceled",
        "market_segment_type_Complementary",
        "market_segment_type_Corporate",
        "room_type_reserved_Room_Type 2",
        "room_type_reserved_Room_Type 3",
        "room_type_reserved_Room_Type 4",
        "type_of_meal_plan_Meal Plan 3",
        "no_of_previous_cancellations",
        "arrival_date",
    ],
    axis=1,
)
In [226]:
pred_test = lgF.predict(X_test) > 0.5
pred_test = np.round(pred_test)
In [227]:
print("Accuracy on training set : ", accuracy_score(y_train, pred_trainF))
print("Accuracy on test set : ", accuracy_score(y_test, pred_test))

Accuracy on training set :  0.7872457827526407
Accuracy on test set :  0.789313959904359

Surprisingly our model did better with the test set than the train set¶

Final Model Summary¶

  • Our final test and train data accuracy is good at around 79% for both
  • There are a few variables that heavily impact wehether the customer is going to cancel their booking or not, refer to Cofficient of Odds Interpretation above for a more detailed breakdown
  • From our ROC curve our regression area is 0.84
  • All VIF values are below 2 after eliminating variables that were causing multicollinearity
  • All variables that were not directly impacting the booking status were removed from the model as per the p-value
  • Overall with good testing and training accuracy, the model serves as a sound predictor for future booking statuses

Building a Decision Tree model¶

In [230]:
# Splitting the data into train and test
XD = df_reg.drop(["booking_status"], axis=1)
YD = df_reg["booking_status"]

# Splitting data in train and test sets
XD_train, XD_test, yD_train, yD_test = train_test_split(
    XD, YD, test_size=0.30, random_state=1
)
In [233]:
dTree = DecisionTreeClassifier(criterion="gini", random_state=1)
dTree.fit(XD_train, yD_train)
Out[233]:
DecisionTreeClassifier(random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(random_state=1)
In [234]:
# Scoring the model
print("Accuracy on training set : ", dTree.score(XD_train, yD_train))
print("Accuracy on test set : ", dTree.score(XD_test, yD_test))

Accuracy on training set :  0.9941667980450891
Accuracy on test set :  0.8738274783888174
In [235]:
## Function to create confusion matrix
def make_confusion_matrix(model, yD_actual, labels=[1, 0]):
    """
    model : classifier to predict values of X
    y_actual : ground truth

    """
    yD_predict = model.predict(XD_test)
    cm = metrics.confusion_matrix(yD_actual, yD_predict, labels=[0, 1])
    df_cm = pd.DataFrame(
        cm,
        index=[i for i in ["Actual - No", "Actual - Yes"]],
        columns=[i for i in ["Predicted - No", "Predicted - Yes"]],
    )
    group_counts = ["{0:0.0f}".format(value) for value in cm.flatten()]
    group_percentages = ["{0:.2%}".format(value) for value in cm.flatten() / np.sum(cm)]
    labels = [f"{v1}\n{v2}" for v1, v2 in zip(group_counts, group_percentages)]
    labels = np.asarray(labels).reshape(2, 2)
    plt.figure(figsize=(10, 7))
    sns.heatmap(df_cm, annot=labels, fmt="")
    plt.ylabel("True label")
    plt.xlabel("Predicted label")
In [236]:
##  Function to calculate recall score
def get_recall_score(model):
    """
    model : classifier to predict values of X

    """
    pred_train = model.predict(XD_train)
    pred_test = model.predict(XD_test)
    print("Recall on training set : ", metrics.recall_score(yD_train, pred_train))
    print("Recall on test set : ", metrics.recall_score(yD_test, pred_test))
In [237]:
make_confusion_matrix(dTree, yD_test)
In [238]:
# Recall on train and test
get_recall_score(dTree)

Recall on training set :  0.9865916437208189
Recall on test set :  0.8117913832199547

Do we need to prune the tree?¶

The model seems to do quite well on test data despite not being pruned, however we can try pruning the tree to see if our accuracy improves further on the test data¶

In [313]:
# Displaying the features used and visualizing the unpruned decision tree

feature_names = list(XD.columns)
print(feature_names)

plt.figure(figsize=(50, 60))
tree.plot_tree(
    dTree,
    feature_names=feature_names,
    filled=True,
    fontsize=7,
    node_ids=True,
    class_names=True,
)
plt.show()

['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'required_car_parking_space', 'lead_time', 'arrival_year', 'arrival_month', 'arrival_date', 'repeated_guest', 'no_of_previous_cancellations', 'no_of_previous_bookings_not_canceled', 'avg_price_per_room', 'no_of_special_requests', 'type_of_meal_plan_Meal Plan 2', 'type_of_meal_plan_Meal Plan 3', 'type_of_meal_plan_Not Selected', 'room_type_reserved_Room_Type 2', 'room_type_reserved_Room_Type 3', 'room_type_reserved_Room_Type 4', 'room_type_reserved_Room_Type 5', 'room_type_reserved_Room_Type 6', 'room_type_reserved_Room_Type 7', 'market_segment_type_Complementary', 'market_segment_type_Corporate', 'market_segment_type_Offline', 'market_segment_type_Online']
In [240]:
# Display rules of the decision tree
print(tree.export_text(dTree, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- lead_time <= 90.50
|   |   |   |   |--- avg_price_per_room <= 215.61
|   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 16.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 162.53
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  162.53
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 169.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  169.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 29.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  29.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  16.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 135.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 115.35
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  115.35
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [31.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  135.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 3 <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [1638.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 3 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |--- lead_time <= 65.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 4.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 59.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  59.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 2.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  2.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 24
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  4.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |--- lead_time >  65.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 99.98
|   |   |   |   |   |   |   |   |--- lead_time <= 75.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 18.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- arrival_date >  18.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [27.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  75.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  87.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  6.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |--- avg_price_per_room >  99.98
|   |   |   |   |   |   |   |   |--- lead_time <= 81.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 123.25
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  123.25
|   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  81.00
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  215.61
|   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |--- lead_time >  90.50
|   |   |   |   |--- lead_time <= 116.50
|   |   |   |   |   |--- avg_price_per_room <= 91.22
|   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 75.07
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 58.75
|   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  58.75
|   |   |   |   |   |   |   |   |   |--- repeated_guest <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- repeated_guest >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  75.07
|   |   |   |   |   |   |   |   |--- arrival_date <= 8.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  8.00
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 83.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  83.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 2.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [56.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.25
|   |   |   |   |   |   |   |   |   |--- lead_time <= 114.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [21.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  114.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.25
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.35
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.35
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [18.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 16.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  16.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |--- avg_price_per_room >  91.22
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 105.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  105.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 138.70
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 65.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  138.70
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 108.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [21.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- avg_price_per_room >  108.50
|   |   |   |   |   |   |   |   |--- lead_time <= 104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [47.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  23.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  104.00
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 110.86
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  110.86
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 111.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  111.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |--- lead_time >  116.50
|   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |--- avg_price_per_room <= 92.50
|   |   |   |   |   |   |   |--- arrival_date <= 23.00
|   |   |   |   |   |   |   |   |--- weights: [5.00, 2.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  23.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  92.50
|   |   |   |   |   |   |   |--- arrival_date <= 11.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  11.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 21.00] class: 1
|   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |--- weights: [124.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 28.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  28.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |--- lead_time <= 125.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 124.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 121.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  121.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- lead_time >  124.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 120.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  120.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 76.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  76.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [12.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  125.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 93.26
|   |   |   |   |   |   |   |   |   |--- lead_time <= 149.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  149.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  93.26
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 96.45
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [14.00, 2.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  96.45
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 118.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  118.78
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- lead_time <= 20.50
|   |   |   |   |--- lead_time <= 3.50
|   |   |   |   |   |--- avg_price_per_room <= 202.67
|   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [62.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 23.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_month >  3.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  23.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 8.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 136.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  136.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 140.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  140.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.66
|   |   |   |   |   |   |   |   |   |   |--- weights: [73.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.66
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 95.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  95.05
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |--- no_of_week_nights >  8.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- avg_price_per_room >  202.67
|   |   |   |   |   |   |--- arrival_month <= 11.00
|   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.00
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |--- lead_time >  3.50
|   |   |   |   |   |--- avg_price_per_room <= 108.20
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [80.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 8.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  8.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 67.61
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  67.61
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- weights: [148.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  108.20
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 195.33
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 111.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  111.67
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  195.33
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 14.00] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [33.00, 0.00] class: 0
|   |   |   |--- lead_time >  20.50
|   |   |   |   |--- avg_price_per_room <= 105.28
|   |   |   |   |   |--- avg_price_per_room <= 60.99
|   |   |   |   |   |   |--- avg_price_per_room <= 29.10
|   |   |   |   |   |   |   |--- weights: [26.00, 0.00] class: 0
|   |   |   |   |   |   |--- avg_price_per_room >  29.10
|   |   |   |   |   |   |   |--- avg_price_per_room <= 41.62
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  41.62
|   |   |   |   |   |   |   |   |--- lead_time <= 84.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [17.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  84.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [13.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |--- avg_price_per_room >  60.99
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 49.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- lead_time >  49.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  71.92
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 85.13
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  85.13
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 38.00] class: 1
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- arrival_month <= 4.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 94.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 15
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  94.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 76.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 21.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  76.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  4.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 104.88
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 74.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  74.45
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  104.88
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  105.28
|   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 74.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 121.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  121.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  74.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 195.19
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 192.92
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  54.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  192.92
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 36.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  36.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 5 >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 22.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  22.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  195.19
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 65.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 48.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  48.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |--- no_of_week_nights <= 9.00
|   |   |   |   |   |   |   |--- weights: [34.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  9.00
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |--- avg_price_per_room <= 215.50
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |--- lead_time <= 91.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 170.27
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 129.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [873.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  129.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 131.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  131.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  170.27
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  91.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 148.00
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 109.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  109.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  148.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 142.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  142.00
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |--- lead_time <= 60.50
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate <= 0.50
|   |   |   |   |   |   |   |   |   |--- weights: [16.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- market_segment_type_Corporate >  0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  60.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |--- avg_price_per_room >  215.50
|   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |--- lead_time <= 4.50
|   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 167.50
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 4.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  4.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 17.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_date >  17.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 153.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  153.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- avg_price_per_room >  167.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 168.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  168.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 181.17
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  181.17
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- lead_time >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [48.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- lead_time >  4.50
|   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 12.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  6.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  12.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.40
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 65.45
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  65.45
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.40
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  26.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_children <= 5.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- no_of_children >  5.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 126.33
|   |   |   |   |   |   |   |   |--- weights: [102.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  126.33
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 153.10
|   |   |   |   |   |   |   |   |   |   |--- weights: [19.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  153.10
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 176.88
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  176.88
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 6.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  6.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 10.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  10.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |--- no_of_weekend_nights <= 2.50
|   |   |   |   |   |   |--- avg_price_per_room <= 118.55
|   |   |   |   |   |   |   |--- lead_time <= 59.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [100.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.30
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |--- weights: [165.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  59.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 77.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  77.85
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- arrival_month >  9.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 14
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |--- avg_price_per_room >  118.55
|   |   |   |   |   |   |   |--- required_car_parking_space <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 20.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 142.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  142.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- arrival_date >  20.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 121.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  121.38
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 13
|   |   |   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 18
|   |   |   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 102.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [79.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  102.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |--- required_car_parking_space >  0.50
|   |   |   |   |   |   |   |   |--- weights: [118.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  2.50
|   |   |   |   |   |   |--- lead_time <= 108.50
|   |   |   |   |   |   |   |--- arrival_month <= 1.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  1.50
|   |   |   |   |   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 50.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 17.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  50.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 85.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  85.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 33.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 9.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  9.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- lead_time >  33.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 144.12
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  144.12
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  108.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- lead_time <= 89.50
|   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |--- weights: [2096.00, 0.00] class: 0
|   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 9.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 133.58
|   |   |   |   |   |   |   |   |   |   |--- weights: [33.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  133.58
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 141.60
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  141.60
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- lead_time >  9.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 19.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  5.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  19.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [32.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |--- weights: [61.00, 0.00] class: 0
|   |   |   |--- lead_time >  89.50
|   |   |   |   |--- avg_price_per_room <= 202.14
|   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 2.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  2.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 26.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 7.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  26.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |--- lead_time <= 150.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 7.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 95.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |--- lead_time >  95.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  25.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  7.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  150.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 95.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 93.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5
|   |   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  93.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 11.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 134.91
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  134.91
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  95.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 9.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Meal Plan 2 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  9.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 130.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  130.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [56.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  202.14
|   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |--- no_of_adults <= 1.50
|   |   |   |   |   |--- lead_time <= 163.50
|   |   |   |   |   |   |--- lead_time <= 162.50
|   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  162.50
|   |   |   |   |   |   |   |--- weights: [0.00, 16.00] class: 1
|   |   |   |   |   |--- lead_time >  163.50
|   |   |   |   |   |   |--- lead_time <= 341.00
|   |   |   |   |   |   |   |--- lead_time <= 173.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [60.00, 6.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 70.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  70.85
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |--- lead_time >  173.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 98.00
|   |   |   |   |   |   |   |   |   |--- arrival_month <= 5.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_month >  5.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 6
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  98.00
|   |   |   |   |   |   |   |   |   |--- lead_time <= 231.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  231.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |--- lead_time >  341.00
|   |   |   |   |   |   |   |--- avg_price_per_room <= 75.00
|   |   |   |   |   |   |   |   |--- arrival_month <= 7.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- arrival_month >  7.00
|   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  75.00
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 88.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  88.00
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 347.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 3.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  347.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 3.00] class: 1
|   |   |   |   |--- no_of_adults >  1.50
|   |   |   |   |   |--- avg_price_per_room <= 83.72
|   |   |   |   |   |   |--- lead_time <= 244.00
|   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 30.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 203.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [5.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  203.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [22.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |--- avg_price_per_room <= 66.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 64.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  64.80
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 10.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 27.77
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  27.77
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [20.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- avg_price_per_room >  66.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 81.76
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 9.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  9.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  81.76
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- lead_time >  244.00
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- arrival_year <= 2017.50
|   |   |   |   |   |   |   |   |   |--- weights: [31.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_year >  2017.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 80.38
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 10
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  80.38
|   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [36.00, 0.00] class: 0
|   |   |   |   |   |--- avg_price_per_room >  83.72
|   |   |   |   |   |   |--- no_of_weekend_nights <= 1.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4
|   |   |   |   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 216.00] class: 1
|   |   |   |   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 94.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  94.52
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_weekend_nights >  1.50
|   |   |   |   |   |   |   |--- arrival_month <= 6.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 12.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  6.00
|   |   |   |   |   |   |   |   |--- weights: [14.00, 0.00] class: 0
|   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |--- avg_price_per_room <= 8.00
|   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |--- arrival_date <= 19.50
|   |   |   |   |   |   |   |--- lead_time <= 205.00
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  205.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |--- arrival_date >  19.50
|   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |--- arrival_date <= 8.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  2.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |--- arrival_date >  8.50
|   |   |   |   |   |   |   |--- weights: [6.00, 0.00] class: 0
|   |   |   |   |--- avg_price_per_room >  8.00
|   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |--- weights: [0.00, 535.00] class: 1
|   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |   |   |   |   |--- lead_time <= 254.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 210.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |   |   |--- lead_time >  210.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |--- lead_time >  254.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 9.00] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 6.00] class: 1
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 54.00] class: 1
|   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- no_of_weekend_nights <= 0.50
|   |   |   |   |--- lead_time <= 180.50
|   |   |   |   |   |--- lead_time <= 159.50
|   |   |   |   |   |   |--- arrival_month <= 8.50
|   |   |   |   |   |   |   |--- weights: [7.00, 0.00] class: 0
|   |   |   |   |   |   |--- arrival_month >  8.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  159.50
|   |   |   |   |   |   |--- arrival_date <= 1.50
|   |   |   |   |   |   |   |--- lead_time <= 176.50
|   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  176.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |--- arrival_date >  1.50
|   |   |   |   |   |   |   |--- no_of_adults <= 0.50
|   |   |   |   |   |   |   |   |--- arrival_date <= 21.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  21.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_adults >  0.50
|   |   |   |   |   |   |   |   |--- weights: [50.00, 0.00] class: 0
|   |   |   |   |--- lead_time >  180.50
|   |   |   |   |   |--- market_segment_type_Offline <= 0.50
|   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 336.00
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 117.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  336.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |   |   |--- lead_time <= 300.50
|   |   |   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 221.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  221.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [11.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  300.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |--- weights: [8.00, 0.00] class: 0
|   |   |   |   |   |--- market_segment_type_Offline >  0.50
|   |   |   |   |   |   |--- no_of_adults <= 2.50
|   |   |   |   |   |   |   |--- lead_time <= 298.00
|   |   |   |   |   |   |   |   |--- weights: [15.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- lead_time >  298.00
|   |   |   |   |   |   |   |   |--- no_of_special_requests <= 1.50
|   |   |   |   |   |   |   |   |   |--- weights: [2.00, 1.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_special_requests >  1.50
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |--- no_of_adults >  2.50
|   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |--- no_of_weekend_nights >  0.50
|   |   |   |   |--- market_segment_type_Online <= 0.50
|   |   |   |   |   |--- lead_time <= 348.50
|   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |--- arrival_date <= 30.50
|   |   |   |   |   |   |   |   |--- weights: [149.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- arrival_date >  30.50
|   |   |   |   |   |   |   |   |--- no_of_week_nights <= 3.00
|   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- no_of_week_nights >  3.00
|   |   |   |   |   |   |   |   |   |--- weights: [1.00, 1.00] class: 0
|   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |--- arrival_month <= 7.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- arrival_month >  7.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  348.50
|   |   |   |   |   |   |--- lead_time <= 372.50
|   |   |   |   |   |   |   |--- avg_price_per_room <= 58.50
|   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- avg_price_per_room >  58.50
|   |   |   |   |   |   |   |   |--- weights: [4.00, 2.00] class: 0
|   |   |   |   |   |   |--- lead_time >  372.50
|   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |--- market_segment_type_Online >  0.50
|   |   |   |   |   |--- arrival_month <= 11.50
|   |   |   |   |   |   |--- avg_price_per_room <= 76.48
|   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.00
|   |   |   |   |   |   |   |   |--- lead_time <= 289.00
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [51.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- arrival_date >  27.00
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 245.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [10.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  245.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  289.00
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |--- avg_price_per_room >  76.48
|   |   |   |   |   |   |   |--- no_of_week_nights <= 5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 248.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 187.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  27.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |--- lead_time >  187.50
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room <= 86.62
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [35.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |   |--- avg_price_per_room >  86.62
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11
|   |   |   |   |   |   |   |   |--- lead_time >  248.50
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |--- lead_time <= 252.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |   |   |--- lead_time >  252.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 8
|   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |--- no_of_week_nights >  5.50
|   |   |   |   |   |   |   |   |--- lead_time <= 204.50
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights <= 3.50
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected <= 0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3
|   |   |   |   |   |   |   |   |   |   |--- type_of_meal_plan_Not Selected >  0.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |--- no_of_weekend_nights >  3.50
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 2.00] class: 1
|   |   |   |   |   |   |   |   |--- lead_time >  204.50
|   |   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |--- arrival_month >  11.50
|   |   |   |   |   |   |--- arrival_date <= 29.50
|   |   |   |   |   |   |   |--- no_of_week_nights <= 8.00
|   |   |   |   |   |   |   |   |--- arrival_date <= 14.50
|   |   |   |   |   |   |   |   |   |--- arrival_date <= 3.00
|   |   |   |   |   |   |   |   |   |   |--- weights: [0.00, 1.00] class: 1
|   |   |   |   |   |   |   |   |   |--- arrival_date >  3.00
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights <= 1.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- no_of_week_nights >  1.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [9.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |--- arrival_date >  14.50
|   |   |   |   |   |   |   |   |   |--- lead_time <= 245.50
|   |   |   |   |   |   |   |   |   |   |--- arrival_date <= 28.00
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2
|   |   |   |   |   |   |   |   |   |   |--- arrival_date >  28.00
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [1.00, 0.00] class: 0
|   |   |   |   |   |   |   |   |   |--- lead_time >  245.50
|   |   |   |   |   |   |   |   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9
|   |   |   |   |   |   |   |   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |   |   |   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_week_nights >  8.00
|   |   |   |   |   |   |   |   |--- weights: [0.00, 3.00] class: 1
|   |   |   |   |   |   |--- arrival_date >  29.50
|   |   |   |   |   |   |   |--- weights: [0.00, 5.00] class: 1
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- no_of_special_requests <= 2.50
|   |   |   |   |--- weights: [0.00, 2135.00] class: 1
|   |   |   |--- no_of_special_requests >  2.50
|   |   |   |   |--- weights: [26.00, 0.00] class: 0
|   |   |--- arrival_month >  11.50
|   |   |   |--- no_of_special_requests <= 0.50
|   |   |   |   |--- weights: [53.00, 0.00] class: 0
|   |   |   |--- no_of_special_requests >  0.50
|   |   |   |   |--- arrival_date <= 24.00
|   |   |   |   |   |--- weights: [4.00, 0.00] class: 0
|   |   |   |   |--- arrival_date >  24.00
|   |   |   |   |   |--- lead_time <= 172.50
|   |   |   |   |   |   |--- weights: [3.00, 0.00] class: 0
|   |   |   |   |   |--- lead_time >  172.50
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 <= 0.50
|   |   |   |   |   |   |   |--- no_of_children <= 0.50
|   |   |   |   |   |   |   |   |--- weights: [2.00, 0.00] class: 0
|   |   |   |   |   |   |   |--- no_of_children >  0.50
|   |   |   |   |   |   |   |   |--- weights: [0.00, 4.00] class: 1
|   |   |   |   |   |   |--- room_type_reserved_Room_Type 4 >  0.50
|   |   |   |   |   |   |   |--- weights: [0.00, 8.00] class: 1
In [242]:
# looking at Gini importance of the columns

print(
    pd.DataFrame(
        dTree.feature_importances_, columns=["Imp"], index=XD_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.358356
avg_price_per_room                    0.172766
market_segment_type_Online            0.092207
arrival_date                          0.079548
no_of_special_requests                0.067472
arrival_month                         0.062923
no_of_week_nights                     0.043018
no_of_weekend_nights                  0.036916
no_of_adults                          0.027805
arrival_year                          0.013883
room_type_reserved_Room_Type 4        0.008165
type_of_meal_plan_Not Selected        0.007607
required_car_parking_space            0.006833
type_of_meal_plan_Meal Plan 2         0.006009
no_of_children                        0.005847
market_segment_type_Offline           0.003194
room_type_reserved_Room_Type 2        0.002290
room_type_reserved_Room_Type 5        0.001362
repeated_guest                        0.001229
no_of_previous_bookings_not_canceled  0.000697
room_type_reserved_Room_Type 6        0.000503
market_segment_type_Corporate         0.000488
room_type_reserved_Room_Type 7        0.000396
market_segment_type_Complementary     0.000182
type_of_meal_plan_Meal Plan 3         0.000181
no_of_previous_cancellations          0.000121
room_type_reserved_Room_Type 3        0.000000
In [253]:
# Generating a plot of features according to their importance
importances = dTree.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="RED", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

Let us limit the depth of the decision tree to 3 and check the accuracy¶

In [246]:
# Creating a decision tree with max depth of 3
dTree1 = DecisionTreeClassifier(criterion="gini", max_depth=3, random_state=1)
dTree1.fit(XD_train, yD_train)
Out[246]:
DecisionTreeClassifier(max_depth=3, random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=3, random_state=1)
In [247]:
# Creating a confusion matrix of the new tree
make_confusion_matrix(dTree1, yD_test)
In [248]:
# Accuracy on train and test
print("Accuracy on training set : ", dTree1.score(XD_train, yD_train))
print("Accuracy on test set : ", dTree1.score(XD_test, yD_test))
# Recall on train and test
get_recall_score(dTree1)

Accuracy on training set :  0.7869698880655841
Accuracy on test set :  0.7855434982527129
Recall on training set :  0.7343469412187238
Recall on test set :  0.7315759637188208

While the accuracy and recall have gone down, the percentages between the training and test data set are much closer to one another making the model more practical overall¶

In [249]:
plt.figure(figsize=(15, 10))

tree.plot_tree(
    dTree1,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=True,
    class_names=True,
)
plt.show()
In [250]:
# Printing the rules of a decision tree -

print(tree.export_text(dTree1, feature_names=feature_names, show_weights=True))

|--- lead_time <= 151.50
|   |--- no_of_special_requests <= 0.50
|   |   |--- market_segment_type_Online <= 0.50
|   |   |   |--- weights: [4628.00, 789.00] class: 0
|   |   |--- market_segment_type_Online >  0.50
|   |   |   |--- weights: [2490.00, 2745.00] class: 1
|   |--- no_of_special_requests >  0.50
|   |   |--- no_of_special_requests <= 1.50
|   |   |   |--- weights: [5666.00, 1034.00] class: 0
|   |   |--- no_of_special_requests >  1.50
|   |   |   |--- weights: [2881.00, 148.00] class: 0
|--- lead_time >  151.50
|   |--- avg_price_per_room <= 100.04
|   |   |--- no_of_special_requests <= 0.50
|   |   |   |--- weights: [670.00, 1254.00] class: 1
|   |   |--- no_of_special_requests >  0.50
|   |   |   |--- weights: [596.00, 236.00] class: 0
|   |--- avg_price_per_room >  100.04
|   |   |--- arrival_month <= 11.50
|   |   |   |--- weights: [26.00, 2135.00] class: 1
|   |   |--- arrival_month >  11.50
|   |   |   |--- weights: [62.00, 12.00] class: 0
In [251]:
# looking at Gini importance of the columns

print(
    pd.DataFrame(
        dTree1.feature_importances_, columns=["Imp"], index=XD_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.504209
market_segment_type_Online            0.194143
no_of_special_requests                0.166143
avg_price_per_room                    0.110694
arrival_month                         0.024811
no_of_week_nights                     0.000000
type_of_meal_plan_Not Selected        0.000000
market_segment_type_Offline           0.000000
market_segment_type_Corporate         0.000000
market_segment_type_Complementary     0.000000
room_type_reserved_Room_Type 7        0.000000
room_type_reserved_Room_Type 6        0.000000
room_type_reserved_Room_Type 5        0.000000
room_type_reserved_Room_Type 4        0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 2        0.000000
type_of_meal_plan_Meal Plan 3         0.000000
required_car_parking_space            0.000000
type_of_meal_plan_Meal Plan 2         0.000000
no_of_children                        0.000000
no_of_previous_bookings_not_canceled  0.000000
no_of_previous_cancellations          0.000000
repeated_guest                        0.000000
arrival_date                          0.000000
arrival_year                          0.000000
no_of_weekend_nights                  0.000000
no_of_adults                          0.000000
In [252]:
# Generating a plot of features according to their importance
importances = dTree1.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10, 10))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

Comparing this graph to the one from our unpruned tree, we can see important variables are not considered such as the arrival date, this can be due to the model being underfit and maybe utilizing more relevant features in constructing our decision tree can lead to better test recall¶

Let us try increasing max depth and see if it improves our test recall¶

In [306]:
# Creating a decision tree with max depth of 16
dTree2 = DecisionTreeClassifier(criterion="gini", max_depth=16, random_state=1)
dTree2.fit(XD_train, yD_train)
Out[306]:
DecisionTreeClassifier(max_depth=16, random_state=1)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=16, random_state=1)
In [307]:
# Creating a confusion matrix of the new tree
make_confusion_matrix(dTree2, yD_test)
In [308]:
# Accuracy on train and test
print("Accuracy on training set : ", dTree2.score(XD_train, yD_train))
print("Accuracy on test set : ", dTree2.score(XD_test, yD_test))
# Recall on train and test
get_recall_score(dTree2)

Accuracy on training set :  0.9398943717483841
Accuracy on test set :  0.8740114033474342
Recall on training set :  0.9002753501735903
Recall on test set :  0.8072562358276644

Our accuracy and recall for train and test data have increased¶

In [312]:
# Visualizing the tree

plt.figure(figsize=(40, 50))

tree.plot_tree(
    dTree2,
    feature_names=feature_names,
    filled=True,
    fontsize=7,
    node_ids=True,
    class_names=True,
)
plt.show()
In [314]:
# looking at Gini importance of the columns

print(
    pd.DataFrame(
        dTree2.feature_importances_, columns=["Imp"], index=XD_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.379431
avg_price_per_room                    0.162222
market_segment_type_Online            0.112882
no_of_special_requests                0.082034
arrival_month                         0.062262
arrival_date                          0.056201
no_of_weekend_nights                  0.033177
no_of_week_nights                     0.031673
no_of_adults                          0.027066
arrival_year                          0.015611
required_car_parking_space            0.007700
type_of_meal_plan_Meal Plan 2         0.006440
type_of_meal_plan_Not Selected        0.005266
room_type_reserved_Room_Type 4        0.004607
market_segment_type_Offline           0.003760
no_of_children                        0.003306
room_type_reserved_Room_Type 2        0.002643
repeated_guest                        0.001414
room_type_reserved_Room_Type 5        0.001244
no_of_previous_bookings_not_canceled  0.000311
room_type_reserved_Room_Type 6        0.000305
market_segment_type_Corporate         0.000223
type_of_meal_plan_Meal Plan 3         0.000222
no_of_previous_cancellations          0.000000
room_type_reserved_Room_Type 3        0.000000
room_type_reserved_Room_Type 7        0.000000
market_segment_type_Complementary     0.000000
In [316]:
# Generating a plot of features according to their importance

importances = dTree2.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(10, 10))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="purple", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

The data model offers a promising test recall along with smaller deviations in recall and accuracy, it also considers key features that the previous tree wasn't able to due to limited depth, we will try one more model and see if the recall can still be improved¶

Cost Complexity Pruning¶

In [268]:
# Creating a dataframe for cost complexity alphas and impurities for the training data

clf = DecisionTreeClassifier(random_state=1)
path = clf.cost_complexity_pruning_path(XD_train, yD_train)
ccp_alphas, impurities = path.ccp_alphas, path.impurities
pd.DataFrame(path)
Out[268]:
ccp_alphas impurities
0 0.000000 0.007381
1 0.000000 0.007381
2 0.000000 0.007381
3 0.000000 0.007381
4 0.000000 0.007381
... ... ...
1344 0.006970 0.285544
1345 0.012982 0.298526
1346 0.017160 0.315686
1347 0.023910 0.363506
1348 0.078164 0.441669

1349 rows × 2 columns

In [272]:
# Creating a plot of leaf impurity vs effective alphas of the decision tree

fig, ax = plt.subplots(figsize=(10, 5))
ax.plot(ccp_alphas[:-1], impurities[:-1], marker="o", drawstyle="steps-post", color="r")
ax.set_xlabel("effective alpha")
ax.set_ylabel("total impurity of leaves")
ax.set_title("Total Impurity vs effective alpha for training set")
plt.show()
In [273]:
# Training the decision tree using generated alphas

clfs = []
for ccp_alpha in ccp_alphas:
    clf = DecisionTreeClassifier(random_state=1, ccp_alpha=ccp_alpha)
    clf.fit(XD_train, yD_train)
    clfs.append(clf)
print(
    "Number of nodes in the last tree is: {} with ccp_alpha: {}".format(
        clfs[-1].tree_.node_count, ccp_alphas[-1]
    )
)

Number of nodes in the last tree is: 1 with ccp_alpha: 0.07816367532161222
In [274]:
# Genrating plots of Number of nodes against alpha value and depth of the decision tree against alpha

clfs = clfs[:-1]
ccp_alphas = ccp_alphas[:-1]

node_counts = [clf.tree_.node_count for clf in clfs]
depth = [clf.tree_.max_depth for clf in clfs]
fig, ax = plt.subplots(2, 1, figsize=(10, 7))
ax[0].plot(ccp_alphas, node_counts, marker="o", drawstyle="steps-post")
ax[0].set_xlabel("alpha")
ax[0].set_ylabel("number of nodes")
ax[0].set_title("Number of nodes vs alpha")
ax[1].plot(ccp_alphas, depth, marker="o", drawstyle="steps-post")
ax[1].set_xlabel("alpha")
ax[1].set_ylabel("depth of tree")
ax[1].set_title("Depth vs alpha")
fig.tight_layout()
In [277]:
train_scores = [clf.score(XD_train, yD_train) for clf in clfs]
test_scores = [clf.score(XD_test, yD_test) for clf in clfs]

fig, ax = plt.subplots(figsize=(10, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("accuracy")
ax.set_title("Accuracy vs alpha for training and testing sets")
ax.plot(ccp_alphas, train_scores, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, test_scores, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
In [278]:
index_best_model = np.argmax(test_scores)
best_model = clfs[index_best_model]
print(best_model)
print("Training accuracy of best model: ", best_model.score(XD_train, yD_train))
print("Test accuracy of best model: ", best_model.score(XD_test, yD_test))

DecisionTreeClassifier(ccp_alpha=4.2041095170529206e-05, random_state=1)
Training accuracy of best model:  0.9731988018287876
Test accuracy of best model:  0.8792532646680155
In [279]:
# Generating data of recall vs alphas for training and testing data

recall_train = []
for clf in clfs:
    pred_trainD = clf.predict(XD_train)
    values_train = metrics.recall_score(yD_train, pred_trainD)
    recall_train.append(values_train)

recall_test = []
for clf in clfs:
    pred_testD = clf.predict(XD_test)
    values_test = metrics.recall_score(yD_test, pred_testD)
    recall_test.append(values_test)
In [280]:
# Plotting for recall vs alpha for training and testing data

fig, ax = plt.subplots(figsize=(15, 5))
ax.set_xlabel("alpha")
ax.set_ylabel("Recall")
ax.set_title("Recall vs alpha for training and testing sets")
ax.plot(ccp_alphas, recall_train, marker="o", label="train", drawstyle="steps-post")
ax.plot(ccp_alphas, recall_test, marker="o", label="test", drawstyle="steps-post")
ax.legend()
plt.show()
In [281]:
# creating the model where we get highest train and test recall
index_best_model = np.argmax(recall_test)
best_model = clfs[index_best_model]
print(best_model)

DecisionTreeClassifier(ccp_alpha=2.3648116033422675e-05, random_state=1)
In [282]:
# Creating a confusion matrix for this model
make_confusion_matrix(best_model, yD_test)
In [283]:
# Recall on train and test
get_recall_score(best_model)

Recall on training set :  0.9867113611875973
Recall on test set :  0.8129251700680272

With Cost Complexity Pruning, we've achieved the highest test data recall at 81.29%¶

In [284]:
# Visualizing the CCP decision tree

plt.figure(figsize=(17, 15))

tree.plot_tree(
    best_model,
    feature_names=feature_names,
    filled=True,
    fontsize=9,
    node_ids=True,
    class_names=True,
)
plt.show()

It is stil a very complex decision tree, however with the large number of variables and booking data, the complexity is to be expected¶

In [286]:
# looking at Gini importance of the columns


print(
    pd.DataFrame(
        best_model.feature_importances_, columns=["Imp"], index=XD_train.columns
    ).sort_values(by="Imp", ascending=False)
)

                                           Imp
lead_time                             0.358882
avg_price_per_room                    0.172550
market_segment_type_Online            0.092650
arrival_date                          0.079076
no_of_special_requests                0.067781
arrival_month                         0.063057
no_of_week_nights                     0.042555
no_of_weekend_nights                  0.036658
no_of_adults                          0.027723
arrival_year                          0.013868
room_type_reserved_Room_Type 4        0.008159
type_of_meal_plan_Not Selected        0.007644
required_car_parking_space            0.006835
type_of_meal_plan_Meal Plan 2         0.006038
no_of_children                        0.005875
market_segment_type_Offline           0.003210
room_type_reserved_Room_Type 2        0.002301
room_type_reserved_Room_Type 5        0.001369
repeated_guest                        0.001235
no_of_previous_bookings_not_canceled  0.000700
room_type_reserved_Room_Type 6        0.000505
market_segment_type_Corporate         0.000445
room_type_reserved_Room_Type 7        0.000398
market_segment_type_Complementary     0.000182
type_of_meal_plan_Meal Plan 3         0.000182
no_of_previous_cancellations          0.000122
room_type_reserved_Room_Type 3        0.000000
In [288]:
# Generating a plot of features according to their importance
importances = best_model.feature_importances_
indices = np.argsort(importances)

plt.figure(figsize=(12, 12))
plt.title("Feature Importances")
plt.barh(range(len(indices)), importances[indices], color="lime", align="center")
plt.yticks(range(len(indices)), [feature_names[i] for i in indices])
plt.xlabel("Relative Importance")
plt.show()

The most important features from the unpruned tree are still considered here therefore a majority of the relevant information is intact after pruning¶

Model Performance Comparison and Conclusions¶

In [310]:
comparison_frame = pd.DataFrame(
    {
        "Model": [
            "Unpruned decision tree model",
            "Decision tree with maximum depth = 3",
            "Decision tree with maximum depth = 16",
            "Decision tree with Cost Complexity Pruning",
        ],
        "Train_Recall": [0.99, 0.73, 0.90, 0.99],
        "Test_Recall": [0.81, 0.73, 0.81, 0.81],
    }
)
comparison_frame
Out[310]:
Model Train_Recall Test_Recall
0 Unpruned decision tree model 0.99 0.81
1 Decision tree with maximum depth = 3 0.73 0.73
2 Decision tree with maximum depth = 16 0.90 0.81
3 Decision tree with Cost Complexity Pruning 0.99 0.81
  • The Unpruned tree while being excellent on training data shows a decent deviation in the recall of testing data, the same applies to the tree with Cost Complexity Pruning
  • The tree with a maximum depth of 3 was too limited and didn't capture all the important variables, because of this test data recall decreased. However for a simple model it still performed soundly
  • The tree model with a maximum depth of 16 showed reduced deviation between train and test recall along with a similar test recall to the unpruned and CCP model, while also being a simpler model than the unpruned tree. It also was able to include and explain the key variables with high Gini importance

Because of this it is recommended to go with the decision tree that has been pruned to a maximum depth of 16 levels¶

Actionable Insights and Recommendations¶

  • What profitable policies for cancellations and refunds can the hotel adopt?
  • What other recommedations would you suggest to the hotel?

Recommendations¶

  • As lead time is the biggest predictor in whether the booking will be cancelled or not, it is recommended to provide scheduled reminders to the guest about the booking and giving them the option to cancel if the time before checkin date is still large, this will help prevent last minute cancellations in case the guest forgot about the booking due to making the booking months in advance while still giving time to the hotel to find another guest to occupy the booking slot
  • Guests that don't select a meal plan are over twice as likely to cancel their booking compared to a guest that chooses a meal plan, you can provide additional meal plan options that appeal to various different diets along with promotions for meal plans depending on the room selection or if the guests book a parking space encouraging them to go for a meal plan
  • Repeated guests are almost twice as likely to not cancel their booking, having a loyalty system to promote repeat customers can lead to reduced booking cancellations
  • The hotel can charge for last minute booking cancellations to alleviate or make up for the4 portion of revenue lost while also discouragin customers from cancelling last minute